Deep learning classification of lipid droplets in quantitative phase images
Name
journal.pone.0249196.pdf
Description
Published version
Size
4.04 MB
Format
Adobe PDF
Checksum (MD5)
3fe5bbb0f02d8bdaa07deab5237312eb
Author(s)
Sheneman, Luke
Stephanopoulos, Gregory
Vasdekis, Andreas E
Date Issued
2021
Journal
PLOS ONE
Publisher
Public Library of Science (PLoS)
Version
Final published version
Abstract
We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0249196
